Nathan Stanley

“I inherited a broken training platform. So I rebuilt it from scratch.”

Rufus's training platform was failing large pretraining runs on 5,000+ GPUs, with no at-scale environment to test fixes in. I rebuilt it from the ground up: a scheduler on AWS Batch and ECS, monitoring across GPU memory, network and I/O, an image caching layer, automated shutdown and restart, and a FUSE-mounted checkpoint filesystem in Rust that removed our checkpoint bottleneck without forcing frameworks onto S3. I drove the roadmap for a team of 10 engineers alongside senior leadership, and earned a cautious science team's trust with incremental wins before landing the bigger rewrites. Availability went from 85% to 99%, every Rufus training job ran on the platform, and it grew to 300+ scientists and engineers. AWS Batch and EC2 adopted our changes, and the platform became the foundation of Amazon's company-wide GPU pooling service.

“Grocery distribution centers were launching in 4 months, and our APIs couldn't take the load.”

Nobody owned the problem end to end, so I took it on. I became the lead for API scaling across Amazon Grocery's distribution center technology org of about 150 people. Over 3 months I coordinated 10 teams and reported weekly to senior leadership. I designed the service boundaries, API contracts and data model across 5 teams, with a shared library so every team implemented them the same way. Redesigning how 15 APIs loaded data with parallel fan-out and caching delivered 20x throughput and 8x lower latency, and we launched in both distribution centers on time. The loading library was later adopted by 15+ teams across Amazon.

“Clubs can't figure out who's causing slow play.”

Hearing that clubs consistently struggled to maintain player speed and identify slowdowns, I invented a novel solution. We tracked players via the scoring app GPS and built a custom algorithm to identify slow players. The frontend resembled a video player over Google Maps where administrators could scrub through any day's timeline and observe player movement. GPS smoothing algorithms provided high-quality signal, and summary reports helped clubs address problematic patterns.

“The legacy system is slow. Make it fast.”

As platform lead, I profiled and optimized MiClub's 2M+ line legacy golf management codebase across the full stack: database engine configuration, query profiling, index optimization, webapp threading, and page load performance. I also built a performance tracker for MiScore to monitor the quality and speed of our OCR scorecard scanning system, reducing scan times from 5 seconds to under 1 second.